Innovative Syste mic Applications€¦ · Ref. M . Janovjak, 15th April 2013 5 InSysteA Innovative...

Ref. M . Janovjak, 15th April 2013 1

InSysteAInnovative Systemic Applications

ISPE Workshop Hamburg, 18.-19.April 2013 „Regulatorischer Druck und Kostendruck in der Pharmaindustrie: Trends zur Veränderung der Produktionsprozesse“ Trend 8: Optimieren der Manufacturing Supply Chain Prozesse durch einen modellbasierenden Ansatz: ein Weg zu besserer Kapitaleffizienz, höherer Geschwindigkeit, Kostenreduzierung und dynamischer Adaptierbarkeit.



Content 1. Challenge

2. Systemic view

3. Model based approach

4. Methodology & Tool 4. Applications (Examples)

5. Conclusions

Optimieren der Manufacturing Supply Chain Prozesse durch einen modellbasierenden Ansatz




2. Systemic view



5. Conclusions




Optimieren der Manufacturing Supply Chain Prozesse durch einen modellbasierenden Ansatz, 1. Challenge

„The complexity of Manufacturing supply chain processes

often overwhelms our ability to understand them.“

In this perspective we are challenged to evolve

an advanced approach to master it.



An advanced approach is needed that enables users - to handle dynamic complexity (timely changes of e.g. product portfolio, disposition of operational processes, trade offs…) - to optimize performance (costs reduction, maximization throughput, OEE, cycle time, process capability…) - to utilize environment (small order sizes, responsiveness, resources, shift / time regime…)

“at the same time” in a “balanced” and “pro-active” way




Such advanced approach - requires a systemic approach, which concentrates on the whole reference system rather than on segments or single events. - calls for understanding the causal structures and generative mechanisms of the manufacturing supply chain which govern its system behavior. - must be based on a dynamic rather than static view (analytical and synthetic).





2. Systemic view



5. Conclusions




demand supply Manufacturing SC

process

Levers

KPI, Benefit potentials System

Optimieren der Manufacturing Supply Chain Prozesse durch einen modellbasierenden Ansatz, 2. Systemic view




process

Levers


Manufacturing SC

process

Manufacturing SC

process

Optimization of assets & capacity utilization; Reduce inventory levels; Reduce Cycle Time; Maximize throughput; Improve Process capability & robustness;

Process design; Policies and principles; Dynamic & linkages of processes; Product portfolio & product quality; Demand (order sizes, scheduling, …)

Process steps & structure; Equipment characterization; Process parameters; Materials; Resources; …

System elements




Levers


Manufacturing SC Process schematics



process

Manufacturing SC

process

Process steps & structure

Equipment characterization

Process parameters

Materials Resources




2. Systemic view



5. Conclusions




How to evolve Model based approach

Such Model based approach requires - dynamic and causal process understanding - consideration of data and expertise - implementation of policies and principles - application of adequate methodology

Build a model of the real system and provide simulation for determination of the best solution.

real system

Optimieren der Manufacturing Supply Chain Prozesse durch einen modellbasierenden Ansatz, 3. Model based approach



Expertise Policies Principles

real system

data, structure,

interactions

Methodology Tool

Model

Simulation results

Modelling

Optimization cycle

Implement best solution




Model is simplified but validated representations of the real system.

Simulation explores the sensitivity of the model behaviour and based on it determines any alternatives and what-if scenarios of the answer to a reel problem.

Model consists all relevant cause-and-effect relationships of the manufacturing SC System components, incl. feedbacks

The simultaneous and complementary (multifunctional) consideration of levers and KPI’s enables to optimize the manufacturing SC Process and to determine the best performing solution.




The Model based approach is one and only way to provide solutions in pro-active way.

The capability to act pro-active opens up additional value proposition - process design - capital efficiency - implementation speed - cost avoidance - risk mitigation

On the purpose, the capability of the applied methodology and tool (SW) has to be of unique performance.





2. Systemic view



5. Conclusions




System Dynamics is a holistic universal applicable Methodology for studying & managing complex feedback systems..

Vensim is a tool (SW) applying SD methodology which enables - to transform knowledge and data into model - to build a model of any complex system - to determine the best performing solution under consideration of the multidimensional optimization policy.

Optimieren der Manufacturing Supply Chain Prozesse durch einen modellbasierenden Ansatz, 4. Methodology & Tool



Vensim Models delivers (SOURCE: VENTANA LTD).

Intelligence amid mixed signals by combining human expertise and data to determine the true signal Focus amid distractions by using cumulative knowledge to highlight new leverage Consensus amid multiple views by integrating knowledge and reconciling differences Understanding amid complexity By tracking the interactions of causes and effects, ... Confidence amid uncertainty by showing the odds and the best ways to influence them while experience deepens

productioninventory control

production rate

inventory

production status

change over


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2. Systemic view



5. Conclusions




Depending on focus (Process, Purpose, Objectives) there is a wide spectrum of possible applications, exemplary: • Manufacturing: demand and supply, material & product flows, … demand & order tracking, WIP / Inventory, throughput, Cycle Time, yield, utilization / OEE, Resources…

• Manufacturing: processing & technology process parameter variations and associated risks, impact on product quality…

• Process development speed / time to market, cost reduction…

• Product Portfolio Life Cycle Management time to market, costs, risks, value generation…

Optimieren der Manufacturing Supply Chain Prozesse durch einen modellbasierenden Ansatz, 5. Applications (Examples)



Manufacturing: demand and supply, material & product flows,… Solid manufacturing & packaging (coated tablets, Lean / WIP, Cycle Time, …)

Biopharmaceuticals bulk manufacturing & fill & finish (organizational design /WIP, CT, …) Liquid fill & finish (syrup, Design & investment efficiency)

Biopharmaceuticals bulk manufacturing & fill & finish (PFS, Lean / WIP, SC responsiveness, …)

Packaging & Palletizing area (demand & performance variations, Eq. design,…)




Manufacturing: processing & technology Process model and risk based multivariate quantitative investigational analysis and improvement of an API process consisting multiple process steps (Dialysis, Ultra filtration , Distillation, Sterile Filtration) Process development Process development and transfer (solid, granulation) Product Portfolio Life Cycle management PPLCM Management and strategic governance (LC Phases II to launch (Pilot Study))

For more information about the 3 Ex. above, please contact InSysteA




Solid manufacturing & packaging

Purpose Build a model which enables to elaborate the Lean optimized manufacturing and packaging process (one certain coated tablet product) Starting position Order based packaging installed, rhythm wheel “optimized “ push practice in bulk manufacturing (at each work center) Approach Objectives Deployment of Pull principle across entire process, optimized campaign size in dispensing & granulation, batch and customer order tracing , back log control, representation of QC & QA process, Eq. utilization Optimization Objectives CT reduction (responsiveness), Cost Reduction (WIP, Inventories, batch yield) Results Cost reduction through reduction of WIP / Inventory ca. 300 k CHF/a while maintaining same customer service KPI




Batch tracing, QC & QA processing

Backlog control per each process step

Model Parameterization

Initialization Pre-position batches

Solid manufacturing & packaging


Pull Order based

Customer order tracing

supply Tabletting Dispensing Granulation

Packaging

Warehouse Coating

Capacity / work center optimized rhythm wheel push practice

Scheduling

demand

Tabletting Dispensing Granulation Coating Packaging

Pull Principle across entire process

25

Total wait time - 25mg (Stacked)200

150

100

50

00 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

day (Day)

SimulatedPullSystem - Dispensing HourSimulatedPullSystem - Granulating HourSimulatedPullSystem - Tabletting HourSimulatedPullSystem - Coating Hour


300

200

100

00 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

day (Day)



300

200

100

00 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

day (Day)



300

200

100

00 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

day (Day)


Mean Cycle Times (Stacked)1,000

500

00 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

day (Day)

Dispensing HourGranulating HourTabletting HourCoating Hour

Total Product at Location (Stacked)20,000

15,000

10,000

5,000

00 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Day

SimulatedPullSystem - Dispensing kgSimulatedPullSystem - Granulating kgSimulatedPullSystem - Tabletting kgSimulatedPullSystem - Coating kgSimulatedPullSystem - Packing kg

25 mg Material Losses (Stacked)200

150

100

50

00 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

day (Day)



300

200

100

00 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

day (Day)



300

200

100

00 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

day (Day)



300

200

100

00 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

day (Day)


Machine Utilisation - (running average)100

50

00 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

day (Day)

SimulatedPullSystem -Granulating PercentSimulatedPullSystem -Tabletting PercentSimulatedPullSystem -Coating PercentSimulatedPullSystem -Packaging Percent

Eq. utilization Idle times WIP mfct.

Cycle Times

Yield losses

Tabletting Dispensing Granulation Coating

Packaging

Simulation 30 days; 90 orders;



Biopharmaceuticals bulk manufacturing & fill & finish Source: Lee Jones, Ventana Ltd.

Purpose Assess impact of change from functional to process-oriented organization given; • Random demand stream for multiple products • Shared production lines • Alternate physical locations of packaging

Approach Study current system and build ‘as is’ SC model; validate Identify performance and structural changes when moving from functional to process-oriented organization (‘to be’) Modify model/parameter values to represent multiple ‘to be’ options Evaluate impact on KPI such as delivery performance, stock holding and cycle times, and determine possible benefit potentials Results Reduction in cycle time of (~25% ) and stocks (~33%) while maintaining same customer service KPI




filling transport bulk storage

finished goods

transport packaging transport delivery

forecast

production campaign

packaging schedule

FTE OEE

Materials

Production campaign drives by forecast

Orders drives packaging schedule

Order data informs forecasting

Orders

Pull bulk for packaging

Pull API through production

Product pushed through filling process

Availabilility of FTE and Materials, packaging line capability influence packaging performance

Product pushed though packaging to finished goods and delivery

Primary contained product

thawing compounding

Biopharmaceuticals bulk manufacturing & fill & finish Source: Lee Jones, Ventana Ltd.


WIP

WIP WIP

WIP

Production campaign drives by pulled scheduling

Order data and WIP Informs scheduling

Orders and order tracing drives order data

Order tracing

Product pulled through filling process



Liquid fill & finish

Purpose Assess the costs reduction potential of currently coupled line and determine the possible benefits in the case of decoupling of the line. Starting position Order based SC installed, optimized rhythm wheel controlled manufacturing (“push”); planned project for line decoupling; Approach Objectives Determination of the solution with biggest cost reduction potential, incl. decision support for project realization. Lean optimization, batch and customer order tracing, representation of QC & QA process, Eq. utilization; Optimization Objectives Cost Reduction (WIP / Inventories, change over costs) Results Cost reduction Lean optimized current coupled line: 25 % Cost reduction Lean optimized decoupled line (planned project): < 30 % Planned project cancelled due to insufficient rentability (cost avoidance 250 kCHF)




Fill + Pack

Fill Pack

(Simulation period = 500 days)

Inventory

Change over

Filling Packaging Costs (%)

0

20

40

60

80

100

Coupled line - Coupled line - Decoupled Decoupled

0

5

10

15

20

25

30

Coupled line - Pull (lean Decoupled line - 0

5

10

15

20

25

30

Coupled line - Rhythm wheel Push Coupled line - Pull (lean optimized current) Decoupled Push on Fill / Pull on Pack Decoupled line - Pull on Fill&Pack

0

20

40

60

80

100

Coupled line - Rhythm wheel P Coupled line - Pull (lean Decoupled line - Push

Total Costs

50%

60%

70%

80%

90%

100%

110%

Coupled line - Rhythm wheel Push Coupled line - Pull (lean optimized current) Decoupled line - Push on Fill / Pull on Pack Decoupled line - Pull on Fill&Pack Decoupled line - Pull on Fill&Pack (QC parallel)

Coupled line - Rhythm wheel Push (current)

Coupled line - Pull (lean optimized )

Decoupled line - Push on Fill ; Pull on Pack Decoupled line - Pull on Fill & Pack Decoupled line - Pull on Fill & Pack and QC parallel

Decoupled line (planned project)

Policy optimized

Coupled line

Cost reduction 25 %

Cost reduction potential < 30 %

Liquid fill & finish




Biopharmaceuticals bulk manufacturing & fill & finish

Purpose Build a model which enables to elaborate the Lean optimized manufacturing SC process Starting position Order based SC installed, BB Cycle Time focused project implemented Approach Objectives Deployment of Pull principle, optimized batch and customer order tracing , back log control, representation of QC & QA process, Eq. utilization Optimization Objectives CT reduction (responsiveness), Cost Reduction (WIP, Inventories) Results Cost reduction through reduction of 4 batches of 24 in WIP Inventory cost reduction ca. 2’000 k CHF/a




Pull principle

Raw materials

Primary Packaging

filters

Compounding and Filling line preparation

Preparation and assembling

Needle Shields

Stoppers

PFS barrel

“Additives II”

“Additives I”

Frozen API

first In line

compounding filling optical inspection packaging

Biopharmaceuticals bulk manufacturing & fill & finish

Model Parameterization

Initialization Pre-position batches

Scheduling Planned

batch orders

customer order tracing batch tracing

Subscripts


Calculations results : Calculations results : Results : - Cycle times at each process steps

- customer order backlogs (duration, # of orders, at delivery) - stock levels WIP at product and at process step (Lt., # of syringes)

- Equipment utilization (OEE)

- mean Cycle times stacked - Total product demand & available supply (released filled product prior sec. packaging)

- Yield / CONC Simulation period 60 days; 469 orders





Biopharmaceuticals fill & finish (PFS, Lean / WIP, SC responsiveness, …)





Packaging & Palletizing area

Purpose Determine performance of packaging area and needed size of conveyor system and palletizer and provide sensitivity analysis of impact of variation of order sizes and lines performance on results. Starting position Upgrade of existing production. Approach Objectives Deployment of random functions to simulate variations of - order sizes - lines performance - change over and line clearance

Optimization Objectives & Results - Determination of impact of the shift to smaller orders sizes on performance and throughput - Determination of material flow (cases) and palettizer utilization (mean, St. Dev.) - Model design adaptable to extended scope (scheduling, order control, WIP…)




Randomized duration change order and share fraction based decision of change order activity

Batch control cycle with determination of cumulative # of batches and change order execution data

Randomized order size and line performance and determination of batch duration and processing time

Packaging line outflow accumulated in cases, conveyor system & transport to Palletizer

Palletizer performance & utilization

Palletizing process as flow rate into buffer in front of Palletizer and warehouse

Packaging line outflow in cases





P40P50P45

max palletising @ 4000060

50

40

30

20max palletising

Palletizer Buffer

6,000

4,500

3,000

1,500

00 10000 20000 30000 40000

Time (Minute)

VVP

Palletizer Buffer : P40Palletizer Buffer : P50Palletizer Buffer : P45

Palletizer outflow per hour

60

45

30

15

00 10000 20000 30000 40000

Time (Minute)

Palle

t/H

our

Palletizer outflow per hour : P40Palletizer outflow per hour : P50Palletizer outflow per hour : P45



Simulation period 30 days; 1’574 orders




2. Systemic view



5. Conclusions

Optimieren der Manufacturing Supply Chain Prozesse durch einen modellbasierenden Ansatz, 5. Conclusions



Benefit of deployment of model based approach with System Dynamics Vensim - enables to master complex manufacturing SC processes and to harvest even “very high hanging fruits” not achievable with other approaches. - leverages user to be in position to elaborate and select optimized solution and making sustainable decisions with minimized risk and maximized benefit. -. results in paradigm shift to facilitate pro – active management of complex manufacturing SC processes. Over all, the universal applicability of the applied methodology & tool opens up to build a platform for a flexible and effective governance to master future challenges.

Optimieren der Manufacturing Supply Chain Prozesse durch einen modellbasierenden Ansatz, 5. Conclusions



Thank You !


Matej Janovjak, M.Sc. S.F.I.T. (Dipl.Ing.ETH) InSysteA GmbH, Innovative Systemic Applications, CEO University of applied Science, Basel, Lecturer for System Dynamics Tel. Mobile +41-79-536 61 56 Tel/Fax +41-52-659 47 22 Address Dorfstr. 56, 8212 Nohl, Switzerland Email [email protected] Web www.insystea.ch

mailto:[email protected]�



Backup slides




Correctness and validity of the model

- Structural fit (structure of the causal relations between variables and sub-processes)

- Behavior correctness (the behavior of the real system is appropriately reproduced by the model, i.e. dynamic and logical behave, equation & operating point)

- Plausibility and consistency of results (qualitative and quantitative, extreme conditions)

- Validity for application (it the model is appropriate for the domain in which it is to be used and conforms with purpose, i.e. based on comparison of the experimental values and simulation results, the model enables required changes and must provide requested answers, i.e. problem solving results )




Optimieren der Manufacturing Supply Chain Prozesse durch einen modellbasierenden Ansatz, 4. Methodology & Tool

The methodological capability and the tool characteristics determine the power of the context of the modelling & simulation

• The methodological capability

- modelling of the dynamic processes - modelling of the complex feedback systems - application field universality - suitable for study and practical management - practical to knowledge mgmt.

• The tool characteristics

- wide range of mathematic functions - easy to model and validate - high calculating capacity - high capability to provide multivariate analysis and simulation - easy customisable front-end cockpit - powerful and easy customisable graphical presentation - integrated data management (data collection, bi-directional IM/IT integration, …)

Methodology capabilities and tool characteristics (in extracts)

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